Biomedical Named Entity Recognition Using Structured Support Vector Machine

Abstract

Named entity recognition is an information extraction subtask that aims to discover named items referenced in unstructured text and classify them into predefined class labels. Identifying biomedical entities such as proteins, cell lines, cell types, DNAs and RNAs has been recognized as a challenging task in named entity recognition. In this paper, the applicability of using structured support vector machine to classify biomedical named entity recognition is thoroughly investigated. This is achieved by utilizing a combination of various types of features such as morphological, part of speech, orthographical, context and word representation to explore the classification performance. Comprehensive experiments were conducted on two popular datasets based on multiple evaluation metrics. Experimental results revealed that the performance of the structured support vector machine surpasses that of different benchmark approaches in the literature.